{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T14:20:50Z","timestamp":1770042050975,"version":"3.49.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002873","name":"Chulalongkorn University","doi-asserted-by":"publisher","award":["HEA663000041"],"award-info":[{"award-number":["HEA663000041"]}],"id":[{"id":"10.13039\/501100002873","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient\u2019s immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and installation instruction for MHCSeqNet2 are available at https:\/\/github.com\/cmb-chula\/MHCSeqNet2.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad780","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:10:49Z","timestamp":1703722249000},"source":"Crossref","is-referenced-by-count":7,"title":["MHCSeqNet2\u2014improved peptide-class I MHC binding prediction for alleles with low data"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6728-1446","authenticated-orcid":false,"given":"Patiphan","family":"Wongklaew","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University , Bangkok 10330, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4117-3632","authenticated-orcid":false,"given":"Sira","family":"Sriswasdi","sequence":"additional","affiliation":[{"name":"Center of Excellence in Computational Molecular Biology, Division of Research Affairs, Faculty of Medicine, Chulalongkorn University , Bangkok 10330, Thailand"},{"name":"Center for Artificial Intelligence in Medicine, Division of Research Affairs, Faculty of Medicine, Chulalongkorn University , Bangkok 10330, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ekapol","family":"Chuangsuwanich","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University , Bangkok 10330, Thailand"},{"name":"Center of Excellence in Computational Molecular Biology, Division of Research Affairs, Faculty of Medicine, Chulalongkorn University , Bangkok 10330, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"2024011122050841100_btad780-B1","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1016\/j.immuni.2019.08.012","article-title":"Defining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction","volume":"51","author":"Abelin","year":"2019","journal-title":"Immunity"},{"key":"2024011122050841100_btad780-B2","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.immuni.2017.02.007","article-title":"Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction","volume":"46","author":"Abelin","year":"2017","journal-title":"Immunity"},{"key":"2024011122050841100_btad780-B3","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1093\/bioinformatics\/btv639","article-title":"Gapped sequence alignment using artificial neural networks: application to the MHC class I system","volume":"32","author":"Andreatta","year":"2015","journal-title":"Bioinformatics"},{"key":"2024011122050841100_btad780-B4","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","article-title":"Enriching word vectors with subword information","volume":"5","author":"Bojanowski","year":"2017","journal-title":"TACL"},{"key":"2024011122050841100_btad780-B5","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1038\/s41587-019-0280-2","article-title":"Predicting HLA class II antigen presentation through integrated deep learning","volume":"37","author":"Chen","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2024011122050841100_btad780-B6","author":"Chung","year":"2014"},{"key":"2024011122050841100_btad780-B7","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/1471-2105-5-113","article-title":"MUSCLE: a multiple sequence alignment method with reduced time and space complexity","volume":"5","author":"Edgar","year":"2004","journal-title":"BMC Bioinformatics"},{"key":"2024011122050841100_btad780-B8","doi-asserted-by":"crossref","first-page":"4684","DOI":"10.1093\/bioinformatics\/btab560","article-title":"Learning embedding features based on multisense-scaled attention architecture to improve the predictive performance of anticancer peptides","volume":"37","author":"He","year":"2021","journal-title":"Bioinformatics"},{"key":"2024011122050841100_btad780-B9","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10930-023-10096-7","article-title":"Align-gram: rethinking the skip-gram model for protein sequence analysis","volume":"42","author":"Ibtehaz","year":"2023","journal-title":"Protein J"},{"key":"2024011122050841100_btad780-B10","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1074\/mcp.TIR119.001656","article-title":"Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework","volume":"18","author":"Karunratanakul","year":"2019","journal-title":"Mol Cell Proteomics"},{"key":"2024011122050841100_btad780-B11","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1093\/protein\/15.4.287","article-title":"Prediction of proteasome cleavage motifs by neural networks","volume":"15","author":"Ke\u015fmir","year":"2002","journal-title":"Protein Eng"},{"key":"2024011122050841100_btad780-B12","doi-asserted-by":"crossref","first-page":"58369","DOI":"10.1109\/ACCESS.2020.2982666","article-title":"Identifying enhancers and their strength by the integration of word embedding and convolution neural network","volume":"8","author":"Khanal","year":"2020","journal-title":"IEEE Access"},{"key":"2024011122050841100_btad780-B13","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1186\/s13046-019-1266-0","article-title":"Advances in cancer immunotherapy 2019 \u2013 latest trends","volume":"38","author":"Kruger","year":"2019","journal-title":"J Exp Clin Cancer Res"},{"key":"2024011122050841100_btad780-B14","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.4049\/jimmunol.1700938","article-title":"Unveiling the peptide motifs of HLA-C and HLA-G from naturally presented peptides and generation of binding prediction matrices","volume":"199","author":"Marco","year":"2017","journal-title":"J Immunol"},{"key":"2024011122050841100_btad780-B15","first-page":"154","article-title":"The toxins of William B. Coley and the treatment of bone and soft-tissue sarcomas","volume":"26","author":"McCarthy","year":"2006","journal-title":"Iowa Orthop J"},{"key":"2024011122050841100_btad780-B16","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1016\/j.humimm.2019.06.009","article-title":"pHLA3D: an online database of predicted three-dimensional structures of HLA molecules","volume":"80","author":"Menezes Teles e Oliveira","year":"2019","journal-title":"Hum Immunol"},{"key":"2024011122050841100_btad780-B17","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1110\/ps.0239403","article-title":"Reliable prediction of T-cell epitopes using neural networks with novel sequence representations","volume":"12","author":"Nielsen","year":"2003","journal-title":"Protein Sci"},{"key":"2024011122050841100_btad780-B18","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.cels.2020.06.010","article-title":"MHCflurry 2.0: improved pan-allele prediction of MHC Class I-presented peptides by incorporating antigen processing","volume":"11","author":"O'Donnell","year":"2020","journal-title":"Cell Syst"},{"key":"2024011122050841100_btad780-B19","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12859-019-2892-4","article-title":"MHCSeqNet: a deep neural network model for universal MHC binding prediction","volume":"20","author":"Phloyphisut","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2024011122050841100_btad780-B20","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s00251-008-0330-2","article-title":"MHC motif viewer","volume":"60","author":"Rapin","year":"2008","journal-title":"Immunogenetics"},{"key":"2024011122050841100_btad780-B21","doi-asserted-by":"crossref","first-page":"W449","DOI":"10.1093\/nar\/gkaa379","article-title":"NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data","volume":"48","author":"Reynisson","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2024011122050841100_btad780-B22","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s41587-019-0322-9","article-title":"A large peptidome dataset improves HLA class I epitope prediction across most of the human population","volume":"38","author":"Sarkizova","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2024011122050841100_btad780-B23","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1074\/mcp.TIR119.001641","article-title":"Mass spectrometry based immunopeptidomics leads to robust predictions of phosphorylated HLA class I ligands","volume":"19","author":"Solleder","year":"2019","journal-title":"Mol Cell Proteomics"},{"key":"2024011122050841100_btad780-B24","doi-asserted-by":"crossref","first-page":"847756","DOI":"10.3389\/fimmu.2022.847756","article-title":"Unsupervised mining of HLA-I peptidomes reveals new binding motifs and potential false positives in the community database","volume":"13","author":"Sricharoensuk","year":"2022","journal-title":"Front Immunol"},{"key":"2024011122050841100_btad780-B25","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.humimm.2020.10.007","article-title":"pHLA3D: updating the database of predicted three-dimensional structures of HLA with HLA-DR, HLA-DQ and HLA-DP molecules","volume":"82","author":"Teles e Oliveira","year":"2021","journal-title":"Hum Immunol"},{"key":"2024011122050841100_btad780-B26","doi-asserted-by":"crossref","first-page":"D480","DOI":"10.1093\/nar\/gkaa1100","article-title":"UniProt: the universal protein knowledgebase in 2021","volume":"49","author":"The UniProt Consortium","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2024011122050841100_btad780-B27","doi-asserted-by":"crossref","first-page":"D339","DOI":"10.1093\/nar\/gky1006","article-title":"The Immune Epitope Database (IEDB): 2018 update","volume":"47","author":"Vita","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2024011122050841100_btad780-B28","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.cell.2020.09.015","article-title":"Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction","volume":"183","author":"Wells","year":"2020","journal-title":"Cell"},{"key":"2024011122050841100_btad780-B29","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3389\/fimmu.2017.00292","article-title":"Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation","volume":"8","author":"Wieczorek","year":"2017","journal-title":"Front Immunol"},{"key":"2024011122050841100_btad780-B30","first-page":"201","article-title":"MHCherryPan. a novel model to predict the binding affinity of pan-specific class I HLA-peptide.","volume-title":"International Journal of Data Mining and Bioinformatics","author":"Xie","year":"2020"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btad780\/54913538\/btad780.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/1\/btad780\/55476424\/btad780.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/1\/btad780\/55476424\/btad780.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T22:05:42Z","timestamp":1705010742000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btad780\/7502956"}},"subtitle":[],"editor":[{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btad780","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,1,1]]},"published":{"date-parts":[[2023,12,28]]},"article-number":"btad780"}}